1.2 Socio-Political Preferences

e-government uptake is also theorised by social preferences for dynamics of intergovernmentalism, the deterrence of international criminal enterprise and the judicious inculcation of the social enterprise.

1.2.1 Intergovernmentalism

In the current planning frontier, Pull Factors are specific to: (i) people profiling, reliant on technologies of social screening and bio-social profiling which has become a normalised technology of intervention [23]; (ii) political bargaining, on the international plane is argued to constitute securing countries of the global north [24], nonetheless, contracting states are open to pursue ‘win-sets’ that maximise national gains, and/or cooperation that may lead to longer-term influence over power relations (ibid); and also (iii) external relations, indicating opportunities for regional bargaining as a nested game of international bargaining (ibid), in addition to advances in the collection, compilation and dissemination of statistics across many national statistics offices which is essential to progress in e-government [25].

By contrast, Push Factors are inflections induced by changes in the state of technology that project over the mid-long term planning horizon. Socio-political preferences for biometrics authentication entails new developments in the areas of: (i) e-Government, such as public procurement [26], and management of internal populations via national identity cards [27]; (ii) the international mobility regime, referring to problems of securing porous borders via an Integrated Risk Management Framework [28] including the management of external populations [29]; and (iii) technological advancements, which are capable of providing symbolic resonance - a ‘theatre of statehood’ – [30] for small states that accrue sovereign power and ‘leapfrog implementations’ [31] by the purchase of security commodities. Furthermore, the trend toward dematerialisation that will ultimately result in digital payment systems is projected to be the future of money, generally [32].

1.2.2 Deterring Criminal Enterprise

In circumventing international crime, the organising logic of biometrics is inherently exclusionary [33] concerning ‘high risk individuals’ associated with business cartels, war crimes, severe human rights violations, organised criminality, and terrorism [34]. For these cases, the risk insurance framework is grounded in a concept of ‘dynamic uncertainty’ [35] that encapsulates the changing mix of strategies and counter-strategies of terrorists as well as the confounding public-private externality that ensues from an act of terrorism in time (t), which itself is associated with the public response to a prior act of terrorism in time (t-1). In such cases, the level of uncertainty and expected exposure to loss may be calculated by the formula: expected loss = ρ ( σ * ψ ), where ρ is the probability of a terrorism event, σ is the risk component, representing standard deviation of expected terrorism catastrophe losses, and ψ equals the maximum loss [36]. However, allocation of responsibility is cast into organisational forms contingent on elements of causal responsibility [37], which may be evaluated by the formula: ( x + y ) z = r, where, the term ( x + y ) refers to the role and capacity responsibility, and the multiplicand z denotes the causal responsibility, while r is the liability responsibility.

1.2.3 Promoting Social Enterprise

Social enterprise as the anti-thesis of criminal enterprise instinctively acquiesces to the prospect of low-risk individuals. However, this is confuted by an emergent ‘State of Exception’ [38] wherein new forms of state power are demonstrably sufficient to monopolise the movement of people and/or criminalise whole populations [39] - a state of affairs evincing political will that has overpowered normative law [40] and the onset of ‘clinicalism of the system’ [41]. In fact it is argued, the social enterprise is liable to become undermined when state processes of exclusion and discrimination manifest behind the veil of biometric authentication [42]. In such a context, the risk insurance framework may be useful to more effectively characterise the perception of heightened risk such as by reference to the Exceedance Probability Curve (EPC). The EPC is constituted by an Aggregate Exceeding Probability (AEP) sub-curve which shows the probability that aggregate losses from all events in a year will exceed a certain level, and the Occurrence Exceeding Probability Event (OEP) sub-curve, which depicts the probability that losses from the single largest hazard event in a year will exceed a certain level [43].

Hence, risk management models may be used to infer social tolerance levels and safety standards aligned with regime rules [44] and biometrics best practice principles [45].

cf. Biopolitics References

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[23] Shamir, 2005; [24] Hira & Cohn, 2004; [5] Finn & Giovannini, 2003; [26] Brewer et al, 2006; [27] Wilson, 2006; [28] Shamir, 2005; [29] Wilson, 2006; [30] Loader, 1999; [31] Edwards, 2008; [32] Tumin, 2002; [33] Wilson, 2006; [34] Harding, 2007; [35] Michel-Kerja, 2003; [36] OECD, 2005; [37] Harding, 2007; [38] Agamben, 2005; [39] Wilson, 2006; [40] Agamben, 2005; [41] Mordini, 2009; [42] Wilson, 2006; [43] OECD, 2005; [44] Gupta, 2010; [45] Pato & Millet, 2010.

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